1. PRINCIPAL COMPONENT ANALYSIS (PCA) PARA A AVALIAÇÃO DE DADOS QUÍMICOS E GERAÇÃO DE HEAT MAPS: UM TUTORIAL
- Author
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Dennis da Silva Ferreira, Leticia da Silva Rodrigues, Fabiola Manhas Verbi Pereira, and Edenir Rodrigues Pereira Filho
- Subjects
exploratory analysis ,data mining ,data visualization ,direct solid sample analysis ,laser ,X-ray fluorescence ,Chemistry ,QD1-999 - Abstract
PRINCIPAL COMPONENT ANALYSIS (PCA) FOR CHEMICAL DATA EVALUATION AND HEAT MAPS PREPARATION: A TUTORIAL. This tutorial shows a step-by-step guide on handling big datasets using principal component analysis (PCA). A dataset of chemical elements’ concentration, emission spectrum, and energy-dispersive X-ray fluorescence (EDXRF) of e-waste were used as examples. Five routines were proposed to apply data processing and PCA calculation focusing data from laser-induced breakdown spectroscopy (LIBS), EDXRF, and heat maps preparation. These routines can be used in various softwares such as MatLab, Octave, R, and Python. PCA was applied in three examples; the first was for concentrations, and the other two were for spectra. An example of heat maps assembling a hyperspectral image of a printed circuit was also described. In addition, a playlist was created on YouTube using the available examples. Therefore, with this tutorial, it may be possible to learn how to deal with a large volume of data by applying PCA. The authors hope to contribute to those researching in the area.
- Published
- 2023
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